Model Anatomy See what's inside open-weight AI models

Can I run bge-reranker-v2.5-gemma2-lightweight-gptq on an RTX 5060?

Probably, at 4-bit

The published weights are too big, but a 4-bit version (about 5.20 GB) would fit, with room for ≈ 6K tokens of context. The site hasn't analyzed a GGUF of bge-reranker-v2.5-gemma2-lightweight-gptq yet.

Full analysis of bge-reranker-v2.5-gemma2-lightweight-gptq Everything that runs on RTX 5060

Files and context on RTX 5060

Each file of bge-reranker-v2.5-gemma2-lightweight-gptq (and of GGUF versions the site has analyzed), and how much context fits next to it, with the context cache in 16-bit or 8-bit.

FileSizeOn RTX 5060 Context (16-bit)Context (8-bit)
As published6.17 GBDoesn't fit3K6K
4-bit (estimate)5.20 GBFits6K8K

bge-reranker-v2.5-gemma2-lightweight-gptq on other hardware

Popular models that run on RTX 5060

Assumes 90% of the card's memory is usable and at least 4K tokens of context for one conversation, with the context cache in 16-bit unless noted. Fits leaves at least 20% of that memory for context; Tight leaves less.